{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target\n",
    "\n",
    "![](http://r-statistics.co/screenshots/ggplot_masterlist_5.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "mpg= pd.read_csv(\"http://goo.gl/uEeRGu\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  border: 1px dotted gray;\n",
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       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
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\"drv\": \"f\", \"year\": 1999, \"model\": \"camry solara\", \"trans\": \"auto(l4)\", \"fl\": \"r\", \"class\": \"compact\", \"manufacturer\": \"toyota\", \"cyl\": 6}, {\"displ\": 3.0, \"hwy\": 26, \"cty\": 18, \"drv\": \"f\", \"year\": 1999, \"model\": \"camry solara\", \"trans\": \"manual(m5)\", \"fl\": \"r\", \"class\": \"compact\", \"manufacturer\": \"toyota\", \"cyl\": 6}, {\"displ\": 3.3, \"hwy\": 27, \"cty\": 18, \"drv\": \"f\", \"year\": 2008, \"model\": \"camry solara\", \"trans\": \"auto(s5)\", \"fl\": \"r\", \"class\": \"compact\", \"manufacturer\": \"toyota\", \"cyl\": 6}, {\"displ\": 1.8, \"hwy\": 30, \"cty\": 24, \"drv\": \"f\", \"year\": 1999, \"model\": \"corolla\", \"trans\": \"auto(l3)\", \"fl\": \"r\", \"class\": \"compact\", \"manufacturer\": \"toyota\", \"cyl\": 4}, {\"displ\": 1.8, \"hwy\": 33, \"cty\": 24, \"drv\": \"f\", \"year\": 1999, \"model\": \"corolla\", \"trans\": \"auto(l4)\", \"fl\": \"r\", \"class\": \"compact\", \"manufacturer\": \"toyota\", \"cyl\": 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\"manufacturer\": \"volkswagen\", \"cyl\": 4}, {\"displ\": 1.8, \"hwy\": 29, \"cty\": 18, \"drv\": \"f\", \"year\": 1999, \"model\": \"passat\", \"trans\": \"auto(l5)\", \"fl\": \"p\", \"class\": \"midsize\", \"manufacturer\": \"volkswagen\", \"cyl\": 4}, {\"displ\": 2.0, \"hwy\": 28, \"cty\": 19, \"drv\": \"f\", \"year\": 2008, \"model\": \"passat\", \"trans\": \"auto(s6)\", \"fl\": \"p\", \"class\": \"midsize\", \"manufacturer\": \"volkswagen\", \"cyl\": 4}, {\"displ\": 2.0, \"hwy\": 29, \"cty\": 21, \"drv\": \"f\", \"year\": 2008, \"model\": \"passat\", \"trans\": \"manual(m6)\", \"fl\": \"p\", \"class\": \"midsize\", \"manufacturer\": \"volkswagen\", \"cyl\": 4}, {\"displ\": 2.8, \"hwy\": 26, \"cty\": 16, \"drv\": \"f\", \"year\": 1999, \"model\": \"passat\", \"trans\": \"auto(l5)\", \"fl\": \"p\", \"class\": \"midsize\", \"manufacturer\": \"volkswagen\", \"cyl\": 6}, {\"displ\": 2.8, \"hwy\": 26, \"cty\": 18, \"drv\": \"f\", \"year\": 1999, \"model\": \"passat\", \"trans\": \"manual(m5)\", \"fl\": \"p\", \"class\": \"midsize\", \"manufacturer\": \"volkswagen\", \"cyl\": 6}, {\"displ\": 3.6, \"hwy\": 26, \"cty\": 17, \"drv\": \"f\", \"year\": 2008, \"model\": \"passat\", \"trans\": \"auto(s6)\", \"fl\": \"p\", \"class\": \"midsize\", \"manufacturer\": \"volkswagen\", \"cyl\": 6}]}, \"encoding\": {\"y\": {\"field\": \"hwy\", \"type\": \"quantitative\"}, \"x\": {\"field\": \"cty\", \"type\": \"quantitative\"}, \"size\": {\"aggregate\": \"count\", \"field\": \"*\"}}, \"config\": {\"cell\": {\"width\": 500, \"height\": 350}, \"mark\": {\"color\": \"#CD4F39\"}}, \"mark\": \"circle\"};\n",
       "var selector = \"#d7d78f77-1557-4368-9ac5-76516c10c8f5\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#d7d78f77-1557-4368-9ac5-76516c10c8f5"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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"
     },
     "metadata": {
      "jupyter-vega": "#d7d78f77-1557-4368-9ac5-76516c10c8f5"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from altair import *\n",
    "chart = Chart(mpg)\n",
    "chart.mark_circle(color='#CD4F39').encode(\n",
    "    y=Y('hwy'),\n",
    "    x=X('cty'),\n",
    "    size=Size('count(*)')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Todo\n",
    "\n",
    "1. The plot looks somewhat different. Am I doing something wrong? The original plot says: Whereever there is more points overlap, the size of the circle gets bigger."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
